Pub Date : 2020-11-01DOI: 10.1109/mce.2020.2997563
Dr Tom George Beaufort Wilson
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Pub Date : 2020-09-01DOI: 10.1109/mce.2020.2986934
A. Fong, M. Usman
The articles in this special section examine machine learning (ML) for end consumers. ML is a discipline that grew out of artificial intelligence (AI). At a minimum, an intelligent agent needs to perceive the environment around it, deliberate, and take the best course of actions to maximize some actual or estimated performance measures. ML was originally a trait of AI that concerned training intelligent agents to perform tasks that cannot be preprogrammed. ML has received much attention recently with advances in technologies that permeate many facets of our everyday lives, e.g., autonomous vehicles, lifelike chatbots, speech synthesis and recognition, intelligent web search, financial forecasting, personal healthcare, traffic navigation, and many other consumer applications. Key enablers that have propelled ML to the forefront of AI research include availability of vast volumes of data, algorithmic advancements that have enabled effective training of deep neural networks, and accessibility and affordability of powerful computing resources. Consequently, novel learning paradigms have been developed beyond the classical discriminative supervised, unsupervised, and semisupervised approaches. Notable novel learning paradigms include reinforcement learning, transfer learning, lifelong learning, generative adversarial learning, and more.
{"title":"Machine Learning for End Consumers","authors":"A. Fong, M. Usman","doi":"10.1109/mce.2020.2986934","DOIUrl":"https://doi.org/10.1109/mce.2020.2986934","url":null,"abstract":"The articles in this special section examine machine learning (ML) for end consumers. ML is a discipline that grew out of artificial intelligence (AI). At a minimum, an intelligent agent needs to perceive the environment around it, deliberate, and take the best course of actions to maximize some actual or estimated performance measures. ML was originally a trait of AI that concerned training intelligent agents to perform tasks that cannot be preprogrammed. ML has received much attention recently with advances in technologies that permeate many facets of our everyday lives, e.g., autonomous vehicles, lifelike chatbots, speech synthesis and recognition, intelligent web search, financial forecasting, personal healthcare, traffic navigation, and many other consumer applications. Key enablers that have propelled ML to the forefront of AI research include availability of vast volumes of data, algorithmic advancements that have enabled effective training of deep neural networks, and accessibility and affordability of powerful computing resources. Consequently, novel learning paradigms have been developed beyond the classical discriminative supervised, unsupervised, and semisupervised approaches. Notable novel learning paradigms include reinforcement learning, transfer learning, lifelong learning, generative adversarial learning, and more.","PeriodicalId":179001,"journal":{"name":"IEEE Consumer Electron. Mag.","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125423241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-01DOI: 10.1109/mce.2020.3002258
S. Mohanty
& I WELCOME THE readers to the 5th issue of year 2020, the September 2020 issue of the IEEE Consumer Electronics Magazine (MCE). We are in the middle of the global impact of the Corona Virus Disease 2019 (COVID-19) for the last several months. The importance of the healthcare system is self-evident along with the other essentials, such as electric power systems, water supply systems, and communication systems, which help to overcome the difficulties. In the July 2020 issue of MCE, we covered Transportation Cyber-Physical System (T-CPS). I am pleased that the current issue is dedicated to Healthcare Cyber-Physical System (H-CPS).
{"title":"Healthcare Cyber-Physical System is More Important Than Before","authors":"S. Mohanty","doi":"10.1109/mce.2020.3002258","DOIUrl":"https://doi.org/10.1109/mce.2020.3002258","url":null,"abstract":"& I WELCOME THE readers to the 5th issue of year 2020, the September 2020 issue of the IEEE Consumer Electronics Magazine (MCE). We are in the middle of the global impact of the Corona Virus Disease 2019 (COVID-19) for the last several months. The importance of the healthcare system is self-evident along with the other essentials, such as electric power systems, water supply systems, and communication systems, which help to overcome the difficulties. In the July 2020 issue of MCE, we covered Transportation Cyber-Physical System (T-CPS). I am pleased that the current issue is dedicated to Healthcare Cyber-Physical System (H-CPS).","PeriodicalId":179001,"journal":{"name":"IEEE Consumer Electron. Mag.","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134178055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-01DOI: 10.1109/mce.2020.3005639
W. Almuhtadi
Reports on the changing of the Consumer Electronics Society to the Changing Technology Society as of August 16, 2020.
截至2020年8月16日，关于消费电子协会向不断变化的技术协会转变的报告。
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Pub Date : 2020-07-01DOI: 10.1109/mce.2020.2972084
Gordana S. Velikic
& THE AUTOMOTIVE INDUSTRY is experiencing disruptive changes at all levels—from design, and production, to community and business models. A previously rigid and very closed business environment is forced to open and embrace new methods to survive. In particular, it has become clear that a previously strongly deterministic approach has to be replaced with flexible adaptive approaches. This opened a path to newmethods, which are reportedly necessary to bring the industry to the ultimate goal—driverless vehicles in any driving condition. The artificial intelligence (AI) has a significant role in this development, but it has not reached the full potential yet due to rigorous requirements that automotive-grade outputs need to satisfy. Nevertheless, an access to a paramount source of data has pushedAImethods to front rows, while all other processing methods are grouped in the “pre-AI era,” or even labeled as “vintagemethods.” Futuristic predictions just a few years ago were very optimistic, and foreseen time span until deployment decreased from several decades to several years. After the first wave of the excitement has passed, the closer look at the whole picture revealed that the problems of the ecosystem apply not just to actual engineering of the vehicles, but to the impacts this technology has on the society. This made us aware, that although we have a technology to answer to the challenge, the technology needs to mature further to cover all critical use cases. An automotive field has always been classified as an industry, rather than consumer, although a consumer mass market of a final product—a vehicle, is huge: according to theworld association of car manufacturers Organisation Internationale des Constructeurs d’Automobiles (OICA), in 2017, the global average annual turnover was 2.75 trillion, with production of 73.4 million cars and 23.84 million trucks. As modern vehicles architecture changes inside, and hardware and software take roles of mechanical parts, so changes the interior of the vehicle. We witness the integration of products and services from creative industries and other common consumer electronics products and services into cabins [1]. This affected terminology and expectations. This nicely illustrates why the term “consumer electronics” (CE) has become obsolete and the term “consumer technology” has become more appropriate, reflecting the social changes due to technology accomplishments and shifts in consumer expectations. Thus, before we hit the big milestone—complete switch to driverless vehicles, also known as L5, we are continuing research to make this experience better, smoother, and safer. The articles in this special section are carefully chosen with the help of the Editor-in-Chief (EIC), are part of this legacy. The articles are extended versions of presentations at the Digital Object Identifier 10.1109/MCE.2020.2972084
{"title":"Intelligent Cars: Are We There Yet?","authors":"Gordana S. Velikic","doi":"10.1109/mce.2020.2972084","DOIUrl":"https://doi.org/10.1109/mce.2020.2972084","url":null,"abstract":"& THE AUTOMOTIVE INDUSTRY is experiencing disruptive changes at all levels—from design, and production, to community and business models. A previously rigid and very closed business environment is forced to open and embrace new methods to survive. In particular, it has become clear that a previously strongly deterministic approach has to be replaced with flexible adaptive approaches. This opened a path to newmethods, which are reportedly necessary to bring the industry to the ultimate goal—driverless vehicles in any driving condition. The artificial intelligence (AI) has a significant role in this development, but it has not reached the full potential yet due to rigorous requirements that automotive-grade outputs need to satisfy. Nevertheless, an access to a paramount source of data has pushedAImethods to front rows, while all other processing methods are grouped in the “pre-AI era,” or even labeled as “vintagemethods.” Futuristic predictions just a few years ago were very optimistic, and foreseen time span until deployment decreased from several decades to several years. After the first wave of the excitement has passed, the closer look at the whole picture revealed that the problems of the ecosystem apply not just to actual engineering of the vehicles, but to the impacts this technology has on the society. This made us aware, that although we have a technology to answer to the challenge, the technology needs to mature further to cover all critical use cases. An automotive field has always been classified as an industry, rather than consumer, although a consumer mass market of a final product—a vehicle, is huge: according to theworld association of car manufacturers Organisation Internationale des Constructeurs d’Automobiles (OICA), in 2017, the global average annual turnover was 2.75 trillion, with production of 73.4 million cars and 23.84 million trucks. As modern vehicles architecture changes inside, and hardware and software take roles of mechanical parts, so changes the interior of the vehicle. We witness the integration of products and services from creative industries and other common consumer electronics products and services into cabins [1]. This affected terminology and expectations. This nicely illustrates why the term “consumer electronics” (CE) has become obsolete and the term “consumer technology” has become more appropriate, reflecting the social changes due to technology accomplishments and shifts in consumer expectations. Thus, before we hit the big milestone—complete switch to driverless vehicles, also known as L5, we are continuing research to make this experience better, smoother, and safer. The articles in this special section are carefully chosen with the help of the Editor-in-Chief (EIC), are part of this legacy. The articles are extended versions of presentations at the Digital Object Identifier 10.1109/MCE.2020.2972084","PeriodicalId":179001,"journal":{"name":"IEEE Consumer Electron. Mag.","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132866350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-05-01DOI: 10.1109/mce.2019.2959747
N. Voros, M. Stan, M. Hübner, G. Keramidas
The current research in VLSI explores emerging trends and novel ideas and concepts covering a broad range of topics in the area of VLSI: from VLSI circuits, systems, and design methods, to system-level design and systemon- chip issues, to bringing VLSI methods to new areas and technologies such as nano and molecular devices, MEMS, and quantum computing. Future design methodologies are also key topics of Very Large Scale Integration (VLSI) research, as well as new Electronic Design Automation (EDA) tools to support them. The purpose of this special section is to provide an insight into current research and development in aspects related to ISVLSI.
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Pub Date : 2020-05-01DOI: 10.1109/mce.2019.2962163
L. Morra, S. Mohanty, F. Lamberti
Artificial intelligence (AI) has become a pillar of consumer electronics. Mobile devices, virtual personal assistants, distributed and wearable sensors, smart home appliances, and automotive electronics are among the many examples of products and services that are benefiting from recent developments in AI. Novel applications, functionalities, and use cases are emerging on an almost daily basis, and providing a comprehensive review within the space of a special issue would be a daunting endeavor. Machine learning currently dominates the AI landscape and this reflects in the submissions that we received, which were mostly related to machine learning and deep learning technologies. We believe that articles selected for this special section well represent emerging applications in sectors with high potential, such as the residential energy and robotic sectors.
{"title":"Artificial Intelligence in Consumer Electronics","authors":"L. Morra, S. Mohanty, F. Lamberti","doi":"10.1109/mce.2019.2962163","DOIUrl":"https://doi.org/10.1109/mce.2019.2962163","url":null,"abstract":"Artificial intelligence (AI) has become a pillar of consumer electronics. Mobile devices, virtual personal assistants, distributed and wearable sensors, smart home appliances, and automotive electronics are among the many examples of products and services that are benefiting from recent developments in AI. Novel applications, functionalities, and use cases are emerging on an almost daily basis, and providing a comprehensive review within the space of a special issue would be a daunting endeavor. Machine learning currently dominates the AI landscape and this reflects in the submissions that we received, which were mostly related to machine learning and deep learning technologies. We believe that articles selected for this special section well represent emerging applications in sectors with high potential, such as the residential energy and robotic sectors.","PeriodicalId":179001,"journal":{"name":"IEEE Consumer Electron. Mag.","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126615135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-05-01DOI: 10.1109/mce.2020.2968754
S. Mohanty
It reminds me that IEEE MCE has covered intelligent electronics, smart electronics kind articles in some of its past issues. In addition, I guest edited a special issue on smart electronics in IEEE Potentials. In Jan 2019 issue of IEEE Potentials, I defined smart electronics as the class C E systems that are envisioned to be Energy-Smart, Security-Smart, and Response-Smart. I advocated that these 3 key aspects and design trade-offs among them is the crucial for the next generation CE. In fact, in my booked titled “Nanoelectronic Mixed-Signal Systems” published in 2015, I presented a broad perspective for design trade-offs of CE systems under the theme “Design of Excellence (DFX)” or ‘Design of X (DFX)”. In DFX, “X” refers to a subset of characteristics/figures-of-merit (FoMs), such as energy, speed, security, and safety, making it Design for Energy, Design for Speed, or Design for Security. Design for Security is essentially the Security and Privacy by Design (SPbD) which was the theme of March 2020 issue of IEEE MCE. We dedicated cover of April 2017 issue of IEEE MCE to deep learning aka deep neural network (DNN). In September 2019 issue of IEEE MCE, we addressed edge-AI, in which AI at the edge devices (close to the user) was highlighted. The current issue (May 2020) of IEEE MCE further advances these efforts on AI. AI is the superset covering machine learning (ML), expert system, and computational intelligence. A subset of AI is machine learning (ML) and a subset of ML is deep learning (DL). Computational intelligence includes artificial neural network (ANN), and a subset of which is deep neural network (DNN).
{"title":"AI for Consumer Electronics - Has Come a Long Way But Has a Long Way to Go","authors":"S. Mohanty","doi":"10.1109/mce.2020.2968754","DOIUrl":"https://doi.org/10.1109/mce.2020.2968754","url":null,"abstract":"It reminds me that IEEE MCE has covered intelligent electronics, smart electronics kind articles in some of its past issues. In addition, I guest edited a special issue on smart electronics in IEEE Potentials. In Jan 2019 issue of IEEE Potentials, I defined smart electronics as the class C E systems that are envisioned to be Energy-Smart, Security-Smart, and Response-Smart. I advocated that these 3 key aspects and design trade-offs among them is the crucial for the next generation CE. In fact, in my booked titled “Nanoelectronic Mixed-Signal Systems” published in 2015, I presented a broad perspective for design trade-offs of CE systems under the theme “Design of Excellence (DFX)” or ‘Design of X (DFX)”. In DFX, “X” refers to a subset of characteristics/figures-of-merit (FoMs), such as energy, speed, security, and safety, making it Design for Energy, Design for Speed, or Design for Security. Design for Security is essentially the Security and Privacy by Design (SPbD) which was the theme of March 2020 issue of IEEE MCE. We dedicated cover of April 2017 issue of IEEE MCE to deep learning aka deep neural network (DNN). In September 2019 issue of IEEE MCE, we addressed edge-AI, in which AI at the edge devices (close to the user) was highlighted. The current issue (May 2020) of IEEE MCE further advances these efforts on AI. AI is the superset covering machine learning (ML), expert system, and computational intelligence. A subset of AI is machine learning (ML) and a subset of ML is deep learning (DL). Computational intelligence includes artificial neural network (ANN), and a subset of which is deep neural network (DNN).","PeriodicalId":179001,"journal":{"name":"IEEE Consumer Electron. Mag.","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127535986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-03-01DOI: 10.1109/mce.2019.2953735
Thinagaran Perumal
& WEB DEVELOPMENT AND programming have gone through a major transformation in recent years. Today’s web development is no longer focused on generic content, but rather ability to display dynamic content across heterogeneous platforms. Internet of Things (IoT) devices is among the new plethora of platforms that are being transformed by web development and programming. Web development and programming for IoT systems are vital as there are many devices that need to display and exchange web content, such as dashboards on mobile apps and wearables. A good example would be Amazon’s Echo with virtual assistant called Alexa. Alexa would be able to search the web with a back-end browser without interfering with front-end interface, clearly an indicator of how IoT is changing the way we deal with the web. Angularjs, Ionic, Laravel, and JavaScript are some of the popular choices needed for web development and programming for IoT devices. Realizing the importance of the trend, the CESoc Malaysia Chapter co-organized a workshop on consumer-centric IoT with the Foundation in Science Department, University of Nottingham Malaysia (UNMC), Semenyih, Malaysia. The workshop themed as “Future of Web Development and Programming” was held on 11th July, 2019. The event saw participation of 16 groups consisting of Foundation in Science students. This is a continuous initiative by the CESoc Malaysia chapter collaborating with academia and industry in Malaysia to expose the technological trends related to consumer-centric IoT. The event is led by Dr. Bavani Ramayah from the School of Foundation in Science, within the same campus. The primary purpose of the event is merely to explore new programming frameworks and web development technologies for IoT devices. Participants presented their working prototypes and these models had been evaluated by Dr. Thinagaran Perumal. Certificates were presented to best three prototypes to the respective groups. The event was a milestone for the Malaysia Chapter in promoting educational outreach. In the future, the CESoc Malaysia Chapter plans to host an extended version of such workshops in other Digital Object Identifier 10.1109/MCE.2019.2953735
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